Bibliographic record
Abstract
PURPOSE OF THE REVIEW: Classification criteria define the patient population for clinical trials and translational studies, but also influence current understanding of the disease. This review attempts to delineate the development from the American College of Rheumatology (ACR) 1982 to the European League Against Rheumatism (EULAR)/ACR 2019 classification criteria for systemic lupus erythematosus (SLE). RECENT FINDINGS: The new EULAR/ACR classification criteria use antinuclear antibodies (ANA) as an entry criterion. (Non-infectious) fever is the one new criterion. All criteria items now have individual weights (from 2 to 10) and are structured in domains, within which only the highest item is counted. There is one common attribution rule, counting criteria only if there is no more likely alternative explanation. Ten points are sufficient for classification. The new criteria have reached a sensitivity of 96.1% and a specificity of 93.4%. The new EULAR/ACR 2019 classification criteria for SLE build on the previous criteria sets, adding fever only as a new criteria item. The new structure is reflective of the current diagnostic approach and has led to improved statistical performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".